1 Healthy lifestyle and the risk of lymphoma in the EPIC study
1
(IJC-19-2849) 2
Sabine Naudin1, Marta Solans Margalef2,3, Fatemeh Saberi Hosnijeh4, Alexandra Nieters5, 3
Cecilie Kyrø6, Anne Tjønneland6,7, Christina C Dahm8, Kim Overvad8,9, Yahya Mahamat- 4
saleh10,11, Caroline Besson10,12, Marie-Christine Boutron-Ruault10,11, Tilman Kühn13, Federico 5
Canzian13, Matthias B. Schulze14,15, Eleni Peppa16, Anna Karakatsani16,17, Antonia 6
Trichopoulou16, Sabina Sieri18, Giovana Masala19, Salvatore Panico20, Rosario Tumino21, 7
Fulvio Ricceri22,23, Sairah Lai Fa Chen24, Leila Luján Barroso25, José María Huerta26,27, Maria- 8
Jose Sánchez27,28,29,30, Eva Ardanaz27,31,32, Virginia Menéndez33, Pilar Amiano Exezarreta27,34, 9
Florentin Spaeth35, Mats Jerkeman36,Karin Jirstom37, Julie A Schmidt38, Dagfinn Aune39,40,41, 10
Elisabete Weiderpass42, Elio Riboli39, Roel Vermeulen4, Delphine Casabonne2,3, Marc 11
Gunter43, Paul Brennan44#, Pietro Ferrari1#*
12
13
1Nutritional Methodology and Biostatistics Group, International Agency for Research on 14
Cancer, World Health Organization, Lyon, France; 2Centro de Investigación Biomédica en 15
Red: Epidemiología y Salud Pública (CIBERESP), Madrid, Spain; 3Unit of Molecular and 16
Genetic Epidemiology in Infections and Cancer, Catalan Institute of Oncology (ICO- 17
IDIBELL), L’Hospitalet de Llobregat, Spain; 4Institute for Risk Assessment Sciences, Division 18
of Environmental Epidemiology, Utrecht University, Utrecht, The Netherlands; 5Research 19
Group Epidemiology, Institute for Immunodeficiency, Medical Center-University of Freiburg, 20
Freiburg, Germany; 6Danish Cancer Society Research Center, Copenhagen, Denmark;
21
7Department of Public Health, Faculty of Health and Medical Sciences, University of 22
Copenhagen; 8Section for Epidemiology, Department of Public Health, Aarhus University, 23
Aarhus, Denmark; 9Department of Cardiology, Aalborg University Hospital, Aalborg, 24
Denmark; 10CESP, Fac. de médecine - Univ. Paris-Sud, Fac. de médecine - UVSQ, INSERM, 25
Université Paris-Saclay, 94805, Villejuif, France; 11Gustave Roussy, F-94805, Villejuif, 26
France; 12Department of Hematology and Oncology, Hospital of Versailles, Le Chesnay, 27
France; 13Division of Cancer Epidemiology, German Cancer Research Center (DKFZ), 28
Heidelberg, Germany; 14Department of Molecular Epidemiology, German Institute of Human 29
Nutrition, Nuthetal, Germany; 15Institute of Nutrition Science, University of Potsdam, 30
Nuthetal, Germany; 16Hellenic Health Foundation, Athens, Greece; 17Pulmonary Medicine 31
Department, School of Medicine, National and Kapodistrian University of Athens, 32
“ATTIKON” University Hospital, Haidari, Greece; 18Epidemiology and Prevention Unit, 33
Fondazione IRCCS Istituto Nazionale dei Tumori di Milano, Milano, Italy; 19Cancer Risk 34
2 Factors and Life-Style Epidemiology Unit, Institute for Cancer Research, Prevention and 35
Clinical Network - ISPRO, Florence, Italy; 20Department of Clinical and Experimental 36
Medecine, University Federico II, Naples, Italy; 21Cancer Registry and Histopathology 37
Department, Azienda Sanitaria Provinciale (ASP) Ragusa, Italy; 22Department of Clinical and 38
Biological Sciences, University of Turin, Italy; 23Unit of Epidemiology, Regional Health 39
Service ASL TO3, Grugliasco (TO), Italy; 24Institutt for Samfunnsmedisin, Det 40
Helsevitenskapelige fakultet, UiT, Norges arktiske universitet, Tromso, Norway; 25Unit of 41
Nutrition and Cancer, Cancer Epidemiology Research Program, Catalan Institute of Oncology 42
(ICO-IDIBELL), Barcelona, Spain; 26Department of Epidemiology, Murcia Regional Health 43
Council, IMIB-Arrixaca, Murcia, Spain; 27Spanish Consortium for Research and Public Health 44
(CIBERESP), Madrid, Spain; 28Andalusian School of Public Health (EASP), Granada, Spain;
45
29Instituto de Investigación Biosanitaria de Granada (ibs.GRANADA). Granada, Spain;
46
30Universidad de Granada (UGR), Granada, Spain; 31Navarra Public Health Institute, 47
Pamplona, Spain; 32IdiSNA, Navarra Institute for Health Research, Pamplona, Spain; 33Public 48
Health Directorate, Asturias, Spain; 34Public Health Division of Gipuzkoa, Biodonostia Health 49
Research Institute, Ministry of Health of the Basque Government, San Sebastian, Spain;
50
35Department of Radiation Sciences, Oncology, Umeå University, Sweden; 36Division of 51
Oncology, Lund University, Malmö, Sweden; 37Laboratory Medicine, Center for Molecular 52
Pathology, Lund University, Malmö, Sweden; 38Cancer Epidemiology Unit, Nuffield 53
Department of Population Health, University of Oxford, Oxford, United Kingdom;
54
39Department of Epidemiology and Biostatistics, School of Public Health, Imperial College 55
London, United Kingdom; 40Department of Nutrition, Bjørknes University College, Oslo, 56
Norway; 41Department of Endocrinology, Morbid Obesity and Preventive Medicine, Oslo 57
University Hospital, Oslo, Norway; 42Office of the Director, International Agency for Research 58
on Cancer, World Health Organization, Lyon, France; 43Nutritional Epidemiology Group, 59
International Agency for Research on Cancer, World Health Organization, Lyon, France;
60
44Genetic Epidemiology Group, International Agency for Research on Cancer, World Health 61
Organization, Lyon, France.
62 63
# Shared Senior Authorship.
64 65
3
*Corresponding Author 66
Pietro Ferrari, PhD 67
Nutritional Methodology and Biostatistics Group 68
International Agency for Research on Cancer, WHO 69
150 cours Albert Thomas 70
69372 Lyon CEDEX 08, France 71
Tel. +33 472 738 031 72
E-mail: ferrarip@iarc.fr 73
74
Key words 75
Hodgkin lymphoma, non-Hodgkin lymphoma, healthy lifestyle index, EPIC, prospective 76
study.
77 78
Abbreviations 79
HLI: healthy lifestyle index 80
CI: confidence interval 81
EPIC: European Prospective Investigation into Cancer and Nutrition 82
NHL: non-Hodgkin lymphoma 83
HL: Hodgkin lymphoma 84
BCL: mature B-cell lymphoma 85
MT/NK: mature T and natural killer-cell lymphoma 86
DLBCL: diffuse large B-cell lymphoma 87
FL: follicular lymphoma 88
CLL/SLL: chronic lymphocytic leukemia and small lymphocytic leukemia 89
PCN/MM: plasma cell neoplasm and multiple myeloma 90
HR: hazard ratio 91
92
4 Article category
93
Short Report, Cancer Epidemiology 94
95
Novelty and impact statements (Words=75) 96
The role of lifestyle factors in the etiology of lymphoma remains unclear and most 97
epidemiological studies faced limited statistical power to evaluate lymphoma subtypes in 98
prospective investigations. In this study, the relationship between a score combining lifestyle 99
exposures and the occurrence of lymphoma subtypes was examined within a large European 100
prospective cohort. Although an inverse association was observed with the risk of Hodgkin 101
lymphoma, findings indicated a limited role of lifestyle factors in lymphoma etiology.
102 103
5 Abstract (Words = 248)
104
Limited evidence exists on the role of modifiable lifestyle factors on the risk of lymphoma. In 105
this work, the associations between adherence to healthy lifestyles and risks of Hodgkin 106
lymphoma (HL) and non-Hodgkin lymphoma (NHL) were evaluated in a large-scale European 107
prospective cohort. Within the European Prospective Investigation into Cancer and Nutrition 108
(EPIC), 2,999 incident lymphoma cases (132 HL and 2,746 NHL) were diagnosed among 109
453,808 participants after 15 years (median) of follow-up. The healthy lifestyle index (HLI) 110
score combined information on smoking, alcohol intake, diet, physical activity and BMI, with 111
large values of HLI expressing adherence to healthy behavior. Cox proportional hazards 112
models were used to estimate lymphoma hazard ratios (HR) and 95% confidence interval (CI).
113
Sensitivity analyses were conducted by excluding, in turn, each lifestyle factor from the HLI 114
score. The HLI was inversely associated with HL, with HR for a 1-standard deviation (SD) 115
increment in the score equal to 0.78 (95%CI: 0.66, 0.94). Sensitivity analyses showed that the 116
association was mainly driven by smoking and marginally by diet. NHL risk was not associated 117
with the HLI, with HRs for a 1-SD increment equal to 0.99 (0.95, 1.03), with no evidence for 118
heterogeneity in the association across NHL subtypes. In the EPIC study, adherence to healthy 119
lifestyles was not associated with overall lymphoma or NHL risk, while an inverse association 120
was observed for HL, although this was largely attributable to smoking. These findings suggest 121
a limited role of lifestyle factors in the etiology of lymphoma subtypes.
122 123
6 Introduction (Words = 2,481)
124
Lymphoma comprises a heterogeneous group of malignancies occurring in the lymphatic 125
system, traditionally grouped as Hodgkin (HL) and non-Hodgkin lymphoma (NHL),1 which 126
accounts for about 3.2% of cancers worldwide.2 During recent decades, lymphomas incidence 127
rates increased with relatively higher rates in high-income countries2 and significant disparities 128
among ethnic groups,3 suggesting an influence of lifestyle factors in lymphomagenesis that are 129
more prevalent in the Western world.
130
Although the roles of lifestyle factors have been extensively investigated in association with 131
solid neoplasms, evidence on lymphoma risk remains unclear.4 Obesity and alcohol 132
consumption have been most consistently associated with lymphoma, with positive5 and 133
inverse6 relationships, respectively. However, most studies, predominantly case-control, faced 134
differential recall bias for the assessment of lifestyle habits and sample size limitations for the 135
investigation of lymphoma subtypes. Additionally, lifestyle factors were often evaluated 136
independently in etiological models.
137
In this study, a set of modifiable exposures, including smoking, alcohol intake, dietary habits, 138
body mass index (BMI), and physical activity were combined into the Healthy Lifestyle Index 139
(HLI) to reflect adherence to healthy habits. The HLI was previously related to the risks of site- 140
specific and overall cancers in prospective studies.7 In this analysis, associations between the 141
HLI and lymphoma risks were examined within the EPIC study. The contributing role of each 142
component of the HLI to lymphoma risk was also investigated.
143 144
Methods 145
Study population. EPIC is a multicenter prospective study designed to investigate the etiology 146
of cancer in relation to diet and lifestyle factors. From 1992 to 2000, a total of 521,324 147
participants (70% women, 35–70 years of age at baseline) were recruited in 10 European 148
countries, mostly from the general population, as explained previously.8 In France, Norway, 149
Utrecht and Naples, only women were recruited. Approval was obtained from IARC and 150
participating institutions’ ethical review boards and participants provided informed consent 151
before completing questionnaires at baseline.
152
Ascertainment of outcome. Cancer cases were identified during follow-up based on population 153
cancer registries in Denmark, Italy, Netherlands, Spain, Sweden, Norway and the United 154
Kingdom, and on a combination of methods, including health insurance records, cancer and 155
pathology registries, and active follow-up of EPIC participants and their next of kin in France, 156
7 Naples, Germany, and Greece. Clinical and morphological data were standardized using a 157
common protocol across centers.8 Mortality data were collected from cancer or mortality 158
registries at the regional or national level.
159
The most recent vital status and cancer diagnosis update was used. Vital status was known for 160
98.4% of all EPIC subjects while 1.6% of participants had emigrated, withdrawn or were lost 161
to follow-up. The follow-up period ended between June 2008 and December 2012 depending 162
on the recruitment centers.7 163
Diagnoses of primary incident lymphoma cases were classified based on the International 164
Classification of Diseases Oncology, 3rd edition (ICD-O-3), and grouped according to 165
recommendations of the InterLymph Pathology Working Group,1 as: Hodgkin lymphoma 166
(HL), non-Hodgkin lymphoma (NHL) and lymphoma not otherwise specified (NOS); within 167
NHL as: mature B-cell lymphoma (BCL), mature T and natural killer-cell lymphoma (MT/NK) 168
and other NHL; among BCL as: diffuse large B-cell lymphoma (DLBCL), follicular lymphoma 169
(FL), chronic lymphocytic leukemia and small lymphocytic leukemia (CLL/SLL), multiple 170
myeloma and plasma cell neoplasm (MM/PCN) and other BCL, as detailed in Table 1.
171
Exposure assessment. Habitual diet, including alcohol intake, during the year preceding 172
recruitment was assessed at recruitment using validated center-specific self-reported dietary 173
questionnaires.8 Data on anthropometry (self-reported in France and the UK Oxford center), 174
physical activity, smoking habits, and prevalent chronic conditions were collected using 175
lifestyle questionnaires.8 176
A diet score was built from the combination of six dietary factors reflecting diet quality,7 i.e.
177
cereal fibers, red and processed meat, the ratio of polyunsaturated to saturated fatty acids, 178
margarine (to express industrially produced trans-fats), glycemic load, and fruits and 179
vegetables. For each dietary factor, country-specific residuals were computed in models with 180
total energy intake, grouped into country-specific deciles and scored from 0 to 9 with 0 being 181
the least healthy (i.e. high intake of red meat/processed meat, margarine, and glycaemic load, 182
and low intake of fruits and vegetables, cereal fibres, and ratio of polyunsaturated to saturated 183
fatty acids). Individual scores were summed up and categorized into quintiles.
184
Definition of HLI. Scores of 0 to 4 were assigned to each individual variable category 185
attributing larger values to the healthier behaviours for smoking (current smoking 186
> 15 cigarettes/day = 0, current smoking ≤15 cigarettes/day = 1, ex-smokers quit ≤ 10-years = 2, 187
ex-smokers quit > 10 years = 3, never smokers = 4), alcohol consumption (in g/day) at 188
recruitment (>48 = 0, 24–47.9= 1, 12–23.9 = 2, 6–11.9 = 3, and <6 = 4), diet score (1st quintile = 0 189
8 to the 5th quintile = 4), physical activity index (inactive=1, moderately inactive=2, moderately 190
active=3, active=4), and body mass index at recruitment (BMI, kg/m2: >30= 0, 26–29.9 = 1, 191
<22=2, 24–25.9 = 3, 22–23.9=4). The final score was the arithmetic sum of the scores for each 192
lifestyle factor and ranged from 1 to 20.
193
Statistical analysis.
194
The association between the HLI and the risk of lymphoma was evaluated using multivariable 195
Cox proportional hazards models, with age as the primary time variable, and Breslow’s method 196
to handle ties. The time at study entry was the age at recruitment, while the exit time was 197
defined as the age at cancer diagnosis, death, loss to, or end of follow-up, whichever occurred 198
first. All models were stratified by country,9 age at recruitment in 1-year categories and sex.
199
The HLI was modelled as a continuous variable to compute HR estimates for a one-standard 200
deviation (SD) corresponding to approximately 3 units in the score, and in quartiles using the 201
second quartile as reference to avoid extreme comparisons within the HLI range. Models were 202
systematically adjusted for education level (no degree/primary school, secondary/technical or 203
professional school, longer education including university degree, unknown (4%)), height (cm, 204
continuous), and energy intake from non-alcohol sources (kcal/day, continuous).
205
Overall tests for statistical significance of HRs were determined by comparing Wald-test 206
statistics to a χ² distribution with three degrees of freedom (dof) for HLI in categories (pWald) 207
and one dof in continuous (ptrend). The assumption of proportional hazards (PH) was evaluated 208
through the Schoenfeld’s residuals.10 209
Potential departure from linearity in the association between HLI and HL risk was evaluated 210
using restricted cubic splines11 and comparing the difference in log-likelihood of models with 211
and without non-linear terms to a χ² distribution with two degrees of freedom.
212
Sensitivity analyses were carried out by excluding, in turn, each factor from the HLI scores to 213
identify factors mostly driving associations with each lymphoma subtype. The excluded 214
component was used as a confounder in the model. Relationships between the HLI and 215
lymphoma risks (HL and NHL) were examined by, in turn, sex, European region (North:
216
Denmark, Norway, Sweden; Central: United Kingdom, The Netherlands, Germany; South:
217
France, Greece, Italy, and Spain), and age at recruitment (<50, 50–60, ≥60 years old).
218
Heterogeneity was evaluated by comparing the difference in log-likelihood of models with and 219
without interaction terms between the HLI (continuous) and, in turn, sex, European region and 220
age categories, to a χ² distribution with dof equal to the total number of interaction terms minus 221
one. Heterogeneity of associations across BCL subtypes was evaluated through data- 222
9 augmentation by comparing the difference in log-likelihood of models with and without an 223
interaction term between the HLI and an indicator variable for BCL subtypes to a χ² distribution 224
with four dof.12 To address potential reverse causation, analyses were carried out excluding the 225
first 2 and 5 years of follow-up.
226
Two-sided p-values were determined with nominal statistical significance set to 5%. Analyses 227
were performed using Stata version 14.13 228
Data availability. Information to access EPIC data and/or biospecimens can be found at 229
http://epic.iarc.fr/access/gain_access.php.
230 231
Results 232
Study participants without lifestyle or dietary information (n=6,902), with a ratio of estimated 233
energy intake to energy requirement in the top or bottom 1% (n=10,241), with self-reported 234
prevalent cancer (n=24,221), with missing follow-up information (n=3,800) and with missing 235
smoking status (n=15,685) or physical activity (n=8,824) were excluded. From a total of 236
453,808 participants followed-up over 15 years (median), with a total of 6,328,639 person- 237
years, 2,999 incident lymphoma cases were diagnosed, including 2,746 NHL, 132 HL and 121 238
lymphomas NOS (Table 1). The HLI components and the confounding variables are described 239
in Table 2. HLI was positively related to level of education and showed higher values in 240
women than men.
241
No association was observed between the HLI and the overall risk of lymphoma (Table 3).
242
However, a 1-SD increase of HLI was inversely associated with HL risk (HR=0.78, 95%CI:
243
0.66, 0.94; ptrend= 7.3e-03). The HRs for HL risk comparing the first, third and fourth quartile 244
to the second quartile were 1.21 (0.78, 1.86), 0.64 (0.37, 1.09), and 0.64 (0.37, 1.10), 245
respectively, with a significant trend across categories (pWald=0.03). The HLI was not 246
associated with the risk of the major NHL subtypes (Table 3). The PH assumption was satisfied 247
in each lymphoma subtype model.
248
The HLI and HL risk dose-response relationship using restricted cubic splines presented 249
limited evidence of departure from linearity (pnon-linearity= 0.42) (Online Supplementary 250
Figure 1).
251
Sensitivity analyses indicated that exclusion of smoking or diet from the HLI resulted in HL 252
HRs for a 1-SD increase equal to 0.88 (95%CI: 0.71,1.10; ptrend=0.27) and 0.85 (0.69,1.04;
253
10 ptrend=0.12), respectively (Online Supplementary Table 1). HRs for the other NHL subtypes 254
were not altered after exclusion of, in turn, each lifestyle factors of the HLI.
255
The associations between the HLIand lymphoma risk did not show evidence of heterogeneity 256
by sex, European region and age at recruitment (results not shown). No evidence for 257
heterogeneity was found across BCL subtypes (pheterogeneity=0.20). Exclusion of the first 2 and 258
5 years of follow-up did not materially alter HR estimates (Online Supplementary Table 2).
259 260
Discussion 261
In a large European prospective study, a score combining five lifestyle factors was not 262
associated with the risk of NHL. An inverse relationship was observed for HL, where smoking 263
and, to a lesser extent, diet were the main drivers of the association.
264
This study is one of the first attempts to investigate the risk of lymphoma with respect to 265
modifiable lifestyle factors combined into a score. Within the NIH-AARP study, a score based 266
on the American Cancer Society recommendations including physical activity, diet, BMI, 267
alcohol, but not smoking, yielded an inverse association between adherence to 268
recommendations and HL risk. A 43% (95%CI: 2%,67%) lower risk of HL was observed when 269
comparing the healthiest with the least healthy score category in an analysis including 113 HL 270
cases, suggesting that lifestyle factors other than smoking may affect HL etiology, while no 271
association was observed with NHL risk, consistently with findings in our study.14 272
Smoking has been consistently positively associated with HL risk,15 with chronic exposure to 273
cigarette smoking believed to promote and support lymphogenic microenvironment and affect 274
immune cells through the impairment of T cells, natural killer cells, B cells and macrophages.16 275
In our work, a comprehensive evaluation of the association between HLI and HL was 276
undertaken via sensitivity analyses where each component of the lifestyle score was, in turn, 277
removed from the HLI. Exclusion of smoking from HLI resulted in a null association 278
suggesting that smoking was largely driving the association between lifestyle factors and HL 279
risk.
280
Although diet has been inconsistently related to HL,17 recent EPIC studies showed that dietary 281
patterns reflecting Mediterranean and anti-inflammatory potential of diet were inversely 282
associated with HL risk.18,19 In our sensitivity analysis a null association was consistently 283
observed after the exclusion of diet from the HLI score, suggesting that diet could be involved 284
in the HLI-HL relationship. Plausible biological mechanisms relating HL pathology to diet may 285
11 involve inflammation pathways, possibly reflecting, among other factors, a diet rich in 286
saturated fat, refined grains, red and processed meat, and high glycemic load.17,20 287
Cumulative evidence points towards a positive relationship between obesity and HL21 which 288
could be the consequence of an alteration of the immune response and stimulate low-grade 289
chronic inflammation in adipose tissue.5 Alcohol intake has been repeatedly inversely 290
associated with risks of HL and NHL, particularly with DLBCL, CLL and FL subtypes,6 a 291
result that was partially attributed to reverse causation, as early symptoms of lymphomas may 292
lead individuals to either quit or reduce their alcohol intake.22 293
Current evidence suggests a role of lifestyle factors with respect to several NHL subtype risks.
294
While smoking has been positively related to T-cell NHL,15 obesity has been related to an 295
increase in diffuse large B-cell lymphoma (DLBC) and multiple myeloma (MM) risks,5 and a 296
pro-inflammatory diet was positively associated with mature B-cell NHL.18 In this study, HLI 297
was not associated with the risk of NHL, either overall or within any of the NHL subtypes.
298
Although HLI was inversely associated with the group of ‘other BCL’ (HR for a 1-SD increase 299
in the HLI: 0.88; 95%CI: 0.79,1.00; ptrend=0.04), the associations of HLI across BCL subtypes 300
was not heterogeneous (pheterogeneity=0.20). Despite the large size of the EPIC cohort, our study 301
was possibly underpowered to detect likely weak associations of lifestyle habits with respect 302
to lymphoma subtypes. Our results were not altered in sensitivity analyses that excluded, in 303
turn, each lifestyle factor from the score.
304
The strength of the current study relies on its prospective multi-country design, which included 305
study populations with heterogeneous lifestyle habits. Among the limitations, we note that 306
EPIC participants represent a healthy proportion of the general population and that risk 307
estimates in our study were likely attenuated. In addition, our analyses did not account for 308
potential changes in lifestyle habits during follow-up, potentially introducing bias in 309
association estimates. These changes may have been the result of incident morbid conditions 310
in ageing study population. Reverse causation could have biased some of our findings, by 311
inducing changes of lifestyle behaviors before recruitment as a result of early symptoms. To 312
partially address this, associations were minimally affected after exclusion of the first two and 313
five years of follow-up. Furthermore, as pathological techniques for lymphoma ascertainment 314
have developed continuously over the last decades, some of the cases of lymphoma subtypes 315
may have been misclassified or simply missed. However, the most recent recommendations for 316
lymphoma ascertainment were used in our study.1,23 Education was used as a proxy for socio- 317
economic status in the adjustment of the models, which may introduce residual confounding.
318
12 Furthermore, the HLI score considered a selected list of lifestyle factors, each of which was 319
given an equal weight. Information on occupation, pesticide exposure, history of participants’
320
infectious diseases (e.g. Human Immunodeficiency Virus, Epstein-Barr virus, and hepatitis 321
viruses), which are known risk factors of lymphoma,24,25 would provide more informative 322
insights of lymphoma etiology. However, information on these factors was available for a 323
limited proportion of the EPIC cohort.
324
In summary, in a large prospective study of European adults, adherence to a combination of 325
healthy lifestyle habits was not associated with the risk of NHL and was inversely related to 326
the risk of HL, with smoking largely driving this association. These findings suggest a limited 327
role of lifestyle factors in the etiology of lymphoma subtypes. However, the HLI accounts for 328
five lifestyle habits, and other environmental factors like pesticides and occupational exposures 329
might be relevant to lymphoma etiology.
330
13 Financial disclosure
331
This work was supported by the Direction Générale de la Santé (French Ministry of Health) 332
(Grant GR-IARC-2003-09-12-01), by the European Commission (Directorate General for 333
Health and Consumer Affairs) and the International Agency for Research on Cancer. The 334
national cohorts are supported by the Danish Cancer Society (Denmark); the Ligue Contre le 335
Cancer, the Institut Gustave Roussy, the Mutuelle Générale de l’Education Nationale and the 336
Institut National de la Santé et de la Recherche Médicale (France); the Deutsche Krebshilfe, 337
the Deutsches Krebsforschungszentrum, and the Federal Ministry of Education and Research 338
(Germany); the Hellenic Health Foundation, the Stavros Niarchos Foundation and the Hellenic 339
Ministry of Health and Social Solidarity (Greece); the Italian Association for Research on 340
Cancer and the National Research Council (Italy); the Dutch Ministry of Public Health, 341
Welfare and Sports, the Netherlands Cancer Registry, LK Research Funds, Dutch Prevention 342
Funds, the Dutch Zorg Onderzoek Nederland, the World Cancer Research Fund and Statistics 343
Netherlands (the Netherlands); the Health Research Fund, Regional Governments of 344
Andalucýa, Asturias, Basque Country, Murcia (project 6236) and Navarra, Instituto de Salud 345
Carlos III, Redes de Investigacion Cooperativa (RD06/0020) (Spain); the Swedish Cancer 346
Society, the Swedish Scientific Council and the Regional Government of Skåne (Sweden);
347
Cancer Research UK (C864/A14136 to EPIC-Norfolk, C570/A16491 and C8221/A19170 to 348
EPIC-Oxford), Medical Research Council (MR/N003284/1 and MC-UU_12015/1 to EPIC- 349
Norfolk, MR/M012190/1 to EPIC-Oxford) (United Kingdom), the Stroke Association, the 350
British Heart Foundation, the Department of Health, the Food Standards Agency, and the 351
Wellcome Trust (UK). This work was part of Sabine Naudin’s PhD at Claude Bernard Lyon I 352
University (France), funded by Région Auvergne Rhône-Alpes, ADR 2016 (France).
353 354
14 Conflict of interest
355
None to declare.
356 357
Acknowledgments 358
We thank Carine Biessy and Bertrand Hemon for their technical support and contribution to 359
this work. We are also grateful to all the EPIC participants who have been part of the project 360
and to the many other members of the study teams who have enabled this research.
361 362
Copyright statements 363
Where authors are identified as personnel of the International Agency for Research on Cancer 364
/ World Health Organization, the authors alone are responsible for the views expressed in this 365
article and they do not necessarily represent the decisions, policy or views of the International 366
Agency for Research on Cancer / World Health Organization.
367
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18 Table 1. Country-specific distribution of study participants, lymphoma cases and the Healthy Lifestyle Index (HLI) in the EPIC cohort.
Lymphoma
subgroups2 NHL subgroups2 BCL subgroups2
Participants PY FUP1 Overall NHL HL BCL MT/NK DLBCL FL CLL
/SLL
MM
/PCN HLI3
Denmark 53,577 794,546 16 613 569 28 493 23 119 74 115 122 11 (9-14)
France 64,086 829,048 15 219 207 11 196 8 39 41 43 42 13 (11-15)
Germany 48,002 498,396 12 227 211 13 168 11 29 20 39 55 12 (10-14)
Greece 24,687 266,336 11 60 56 3 36 2 2 3 12 15 11 (9-13)
Italy 44,274 627,018 15 296 272 15 216 11 37 32 44 73 11 (9-13)
Norway 29,689 395,178 14 146 141 5 115 14 22 27 23 23 13 (12-15)
Spain 39,855 635,751 17 239 220 14 192 10 33 27 51 51 12 (10-14)
Sweden 47,536 782,458 18 504 436 13 333 20 56 47 72 128 12 (10-14)
The Netherlands 30,555 430,017 15 167 160 6 143 8 37 24 29 38 13 (11-15) United Kingdom 71,547 1,069,891 16 528 474 24 398 18 87 68 81 106 13 (11-15) Total 453,808 6,328,639 15 2,999 2,746 132 2,290 125 461 363 509 653 12 (10-14) Abbreviations: PY, person-years; FUP, follow-up (years); HLI, healthy lifestyle index; NL, The Netherlands; PY, person-years; UK: United Kingdoms; NHL, non-Hodgkin lymphoma; HL, Hodgkin lymphoma; BCL, mature B-cell lymphoma; MT/NK, Mature T and natural killer-cell lymphoma; DLBCL, diffuse large B-cell lymphoma; FL, follicular lymphoma; CLL/SLL, chronic lymphocytic leukemia, small lymphocytic leukemia and prolymphocytic lymphocytic leukemia ; MM/PCN, plasma cell neoplasm and multiple myeloma.
1 Median values;
2 The groups of overall number of lymphoma, NHL and BCL also included lymphomas not otherwise specified (n=121), other NHL subtypes (n=331) and other BCL subtypes (n=304), respectively;
3 Means (25th-75th percentiles).
19 Table 2. Baseline characteristics1 of the EPIC participants by quartiles of Healthy Lifestyle Index (HLI).
Total cohort HLI
Q1 [1 - 10] Q2 [11 - 12] Q3 [13 - 14] Q4 [15 - 20]
Total participants (n) 453,808 129,429 111,358 110,730 102,291
Lymphoma cases (n) 2,999 937 734 718 610
Index components
Smoking (% never) 45 15 40 56 74
Alcohol intake (g/day) 5 (1 - 15) 13 (3 - 30) 6 (1 - 15) 4 (1 - 11) 3 (0 - 7)
BMI (kg/m²) 25 (22 - 28) 27 (24 - 30) 26 (22 - 28) 24 (22 - 27) 23 (22 - 25)
Diet score (units) 27 (23 - 32) 23 (20 - 27) 26 (22 - 30) 28 (24 - 33) 32 (28 - 36)
Physical activity (% active) 18 9 14 19 34
Covariates
Sex (% women) 70 56 71 77 80
Age at recruitment (years) 52 (45 - 58) 52 (46 - 59) 52 (46 - 59) 51 (45 - 58) 50 (44 - 57)
Energy intake from food (kcal/day) 1,921 (1,572 - 2,339)
1,964 (1,597 - 2,401)
1,918 (1,568 - 2,337)
1,901 (1,559 - 2,308)
1,896 (1,565 - 2,296)
Height (cm) 165 (160 - 172) 167 (160 - 174) 165 (159 - 171) 165 (159 - 171) 165 (160 - 171)
Educational level (% higher education) 24 20 22 25 30
1 Medians (25th - 75th percentiles) are presented for continuous variables, percentages for categorical variables.
20 Table 3. Hazard ratio estimates1 for associations between the Healthy Lifestyle Index (HLI) (in quartiles and in continuous for a 1-SD increase2) and risks of lymphoma subtypes in the EPIC study.
HLI
Q1 [1 - 10] Q2 [11 - 12] Q3 [13 - 14] Q4 [15 - 20] pWald3 1-SD increase ptrend3
All lymphomas (n=2,999)
n 937 734 718 610
HR (95% CI) 1.04 (0.94 - 1.14) 1.00 (Ref) 1.02 (0.92 - 1.13) 0.97 (0.87 - 1.08) 0.68 0.98 (0.94 - 1.01) 0.23 HL (n=132)
n 53 36 22 21
HR (95% CI) 1.21 (0.78 - 1.86) 1.00 (Ref) 0.64 (0.37 - 1.09) 0.64 (0.37 - 1.10) 0.03 0.78 (0.66 - 0.94) 7.3E-03 NHL (n=2,746)
n 846 669 668 563
HR (95% CI) 1.02 (0.92 - 1.14) 1.00 (Ref) 1.04 (0.93 - 1.16) 0.98 (0.88 - 1.10) 0.78 0.99 (0.95 - 1.03) 0.50 MT/NK (n=125)
n 42 25 24 34
HR (95% CI) 1.77 (0.62 - 5.01) 1.00 (Ref) 0.75 (0.49 - 1.14) 1.44 (0.85 - 2.44) 0.29 1.04 (0.86 - 1.26) 0.68 BCL (n=2,290)
n 692 564 565 469
HR (95% CI) 1.00 (0.89 - 1.11) 1.00 (Ref) 1.04 (0.93 - 1.17) 0.97 (0.85 - 1.09) 0.69 0.99 (0.95 - 1.04) 0.81 DLBCL (n=461)
n 140 117 103 101
HR (95% CI) 0.98 (0.76 - 1.25) 1.00 (Ref) 0.91 (0.7 - 1.19) 0.98 (0.75 - 1.28) 0.91 0.99 (0.90 - 1.09) 0.84 FL (n=363)
n 88 92 97 86
HR (95% CI) 0.82 (0.61 - 1.10) 1.00 (Ref) 1.04 (0.78 - 1.38) 0.98 (0.73 - 1.32) 0.44 1.04 (0.93 - 1.16) 0.49 CLL/SLL (n=509)
n 171 100 127 111
HR (95% CI) 1.33 (1.04 - 1.71) 1.00 (Ref) 1.35 (1.04 - 1.75) 1.34 (1.02 - 1.77) 0.08 1.05 (0.96 - 1.15) 0.28 MM/PCN (n=653)
n 190 169 179 115
HR (95% CI) 0.91 (0.74 - 1.13) 1.00 (Ref) 1.12 (0.91 - 1.38) 0.83 (0.65 - 1.05) 0.06 0.99 (0.91 - 1.07) 0.73 Other BCL4 (n=304)
n 103 86 59 56
HR (95% CI) 0.96 (0.72 - 1.29) 1.00 (Ref) 0.71 (0.51 - 0.99) 0.75 (0.53 - 1.06) 0.12 0.88 (0.79 - 1.00) 0.04
21 Abbreviations: HLI, Healthy Lifestyle Index; HL, Hodgkin lymphoma; NHL, non-Hodgkin lymphoma; MT/NK, Mature T and natural killer-cell lymphoma; BCL, mature B-cell lymphoma; DLBCL, diffuse large B-cell lymphoma; FL, follicular lymphoma; CLL/SLL, chronic lymphocytic leukemia, small lymphocytic leukemia and prolymphocytic lymphocytic leukemia ; MM/PCN, plasma cell neoplasm and multiple myeloma.
1 Models were adjusted for education level, height, and non-alcohol energy intakes, and stratified by country, age in 1-year category, and sex;
2 One standard deviation corresponded to 3 units in the HLI score;
3 P-values were determined using a Wald test for overall significance, according to a χ2 distribution with three degrees of freedom for evaluation by quartiles, and one degree of freedom for evaluation in continuous.
4 Other BCL includes Burkitt lymphoma, hairy cell leukemia, lymphoplasmacytic lymphoma, Mantle cell lymphoma, marginal zone lymphoma, primary effusion lymphoma and prolymphocytic leukemia subtypes.
22 Healthy lifestyle and the risk of lymphoma in the EPIC study
(IJC-19-2849)
Sabine Naudin, Marta Solans Margalef, Fatemeh Saberi Hosnijeh, Alexandra Nieters, Cecilie Kyrø, Anne Tjønneland, Christina C Dahm, Kim Overvad, Yahya Mahamat-saleh, Caroline Besson, Marie-Christine Boutron-Ruault, Tilman Kühn, Federico Canzian, Matthias B.
Schulze, Eleni Peppa, Anna Karakatsani, Antonia Trichopoulou, Sabina Sieri, Giovana Masala, Salvatore Panico, Rosario Tumino, Fulvio Ricceri, Sairah Lai Fa Chen, Leila Luján Barroso, José María Huerta, Maria-Jose Sánchez, Eva Ardanaz, Virginia Menéndez, Pilar Amiano Exezarreta, Florentin Spaeth, Mats Jerkeman,Karin Jirstom, Julie A Schmidt, Dagfinn Aune, Elisabete Weiderpass, Elio Riboli, Roel Vermeulen, Delphine Casabonne, Marc Gunter, Paul Brennan, Pietro Ferrari
Table of content for online supplementary material
- Online Supplementary Figure 1. Hodgkin lymphoma (HL) hazard ratios (solid line) and corresponding 95% confidence interval (dashed line) as a function of the healthy lifestyle index (HLI) score and the risk of Hodgkin lymphoma (HL)
- Online Supplementary Table 1. Hazard ratio estimates for the associations between a 1-SD increment of Healthy Lifestyle Index (HLI) and the risks of Hodgkin lymphoma (HL) and non-Hodgkin lymphoma (NHL) after excluding, in turn, each lifestyle factor from the HLI
- Online Supplementary Table 2. Hodgkin Lymphoma (HL) and non-Hodgkin Lymphoma (NHL) hazard ratio estimates for a 1-SD increase in the Healthy Lifestyle Index (HLI) after exclusion of the first 2 and 5 years of follow-up.
23 Online Supplementary Figure 1. Hodgkin lymphoma (HL) hazard ratios (solid line) and corresponding 95% confidence interval (dashed line) as a function of the healthy lifestyle index (HLI) score and the risk of Hodgkin lymphoma (HL)1
1 Hazard ratios estimated in Cox models including restricted cubic splines with four internal knots placed at HLI score values of 7, 11, 13 and 17. Departure from linearity was evaluated by comparing the difference in log-likelihood of models with and without non-linear terms to a χ² distribution with two degrees of freedom.
24 Online Supplementary Table 1. Hazard ratio estimates for the associations between a 1-SD increment of Healthy Lifestyle Index (HLI)1 and the risks of Hodgkin lymphoma (HL) and non-Hodgkin lymphoma (NHL) after excluding, in turn, each lifestyle factor from the HLI
HR2 (95% CI) ptrend3 HR2 (95% CI) ptrend3
All lymphoma (n=2,999) DLBCL (n=461)
HLI without Smoking 0.98 (0.93 - 1.02) 0.30 HLI without Smoking 0.98 (0.87 - 1.10) 0.70
HLI without Alcohol 0.97 (0.93 - 1.01) 0.18 HLI without Alcohol 0.95 (0.85 - 1.05) 0.30
HLI without BMI 0.98 (0.94 - 1.03) 0.44 HLI without BMI 1.02 (0.92 - 1.14) 0.69
HLI without Diet 0.98 (0.94 - 1.02) 0.38 HLI without Diet 1.03 (0.92 - 1.15) 0.59
HLI without Physical activity 0.97 (0.93 - 1.02) 0.21 HLI without Physical activity 0.99 (0.89 - 1.10) 0.84
HL (n=132) FL (n=363)
HLI without Smoking 0.88 (0.71 - 1.10) 0.27 HLI without Smoking 1.03 (0.9 - 1.18) 0.64
HLI without Alcohol 0.70 (0.58 - 0.85) 3.50E-04 HLI without Alcohol 1.05 (0.94 - 1.19) 0.39
HLI without BMI 0.80 (0.65 - 0.97) 0.02 HLI without BMI 1.04 (0.92 - 1.17) 0.57
HLI without Diet 0.85 (0.69 - 1.04) 0.12 HLI without Diet 1.03 (0.90 - 1.17) 0.68
HLI without Physical activity 0.75 (0.62 - 0.91) 3.60E-03 HLI without Physical activity 1.04 (0.92 - 1.17) 0.56
NHL (n=2,746) CLL/SLL (n=509)
HLI without Smoking 0.97 (0.93 - 1.02) 0.29 HLI without Smoking 1.05 (0.94 - 1.17) 0.37
HLI without Alcohol 0.98 (0.94 - 1.03) 0.44 HLI without Alcohol 1.07 (0.97 - 1.18) 0.20
HLI without BMI 0.99 (0.95 - 1.04) 0.78 HLI without BMI 1.05 (0.94 - 1.16) 0.40
HLI without Diet 0.99 (0.95 - 1.04) 0.75 HLI without Diet 1.06 (0.95 - 1.18) 0.28
HLI without Physical activity 0.99 (0.94 - 1.03) 0.50 HLI without Physical activity 1.03 (0.93 - 1.14) 0.58
MT / NK cell (n=125) PCN/MM (n=653)
HLI without Smoking 1.03 (0.83 - 1.29) 0.77 HLI without Smoking 0.94 (0.85 - 1.04) 0.21
HLI without Alcohol 1.07 (0.87 - 1.31) 0.52 HLI without Alcohol 0.98 (0.89 - 1.06) 0.58
HLI without BMI 0.93 (0.75 - 1.14) 0.48 HLI without BMI 1.04 (0.95 - 1.14) 0.44
HLI without Diet 0.95 (0.76 - 1.17) 0.61 HLI without Diet 0.98 (0.89 - 1.08) 0.74
HLI without Physical activity 0.94 (0.77 - 1.15) 0.55 HLI without Physical activity 0.98 (0.90 - 1.08) 0.71
BCL (n=2,290) Other BCL4 (n=304)
HLI without Smoking 0.98 (0.93 - 1.03) 0.39 HLI without Smoking 0.88 (0.77 - 1.02) 0.09
HLI without Alcohol 0.99 (0.94 - 1.03) 0.58 HLI without Alcohol 0.88 (0.77 – 1.00) 0.05
HLI without BMI 1.01 (0.96 - 1.06) 0.64 HLI without BMI 0.87 (0.76 – 1.00) 0.04
HLI without Diet 1.00 (0.95 - 1.06) 0.87 HLI without Diet 0.90 (0.78 - 1.03) 0.12
HLI without Physical activity 0.99 (0.94 - 1.04) 0.67 HLI without Physical activity 0.89 (0.78 - 1.02) 0.08
25 Abbreviations: HLI, Healthy Lifestyle Index; HL, Hodgkin lymphoma; NHL, non-Hodgkin lymphoma; MT/NK, Mature T and natural killer-cell lymphoma; BCL, mature B-cell lymphoma; DLBCL, diffuse large B-cell lymphoma; FL, follicular lymphoma; CLL/SLL, chronic lymphocytic leukemia, small lymphocytic leukemia and prolymphocytic lymphocytic leukemia ; MM/PCN, plasma cell neoplasm and multiple myeloma.
1 One Standard deviation corresponded to 3 points of HLI;
2 Models evaluating associations between the HLI and risks of lymphoma were adjusted for education level, non-alcohol energy intakes, height, and the index components currently excluded from the calculation of the HLI, and stratified by study center, age and sex;
3 P-values for trend were determined using a Wald test for overall significance, according to a χ2 distribution with one degree of freedom.
4 Other BCL includes Burkitt lymphoma, hairy cell leukemia, lymphoplasmacytic lymphoma, Mantle cell lymphoma, marginal zone lymphoma, primary effusion lymphoma and prolymphocytic leukemia subtypes.
